{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyspark,pandas,datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.storagelevel import StorageLevel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = pyspark.SparkContext()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据请见./data/readme.md"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdd = sc.textFile(\"e:/110000.csv\")\\\n",
    ".persist(StorageLevel.MEMORY_AND_DISK_SER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2016-01-05 04:59:59,7d0971332e86c3e2920553e87044ac0a,116.580736,39.874868',\n",
       " '2016-01-05 05:00:00,a75008e5add9e088d5bd835b643f7d61,116.30374,40.044107',\n",
       " '2016-01-05 05:00:01,6403bc7585fb858d62b34299ed6fcfc4,116.398001,39.85221',\n",
       " '2016-01-05 05:00:00,47188e01448cedea702b100a504fe6ac,116.358788,39.854221',\n",
       " '2016-01-05 04:59:52,059475a2e6b75e35d5049b4e8f584396,116.011381,39.694151']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdd.take(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 总记录数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "343862"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdd.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = rdd.map(lambda line : (line.split(\",\")[1],1))\\\n",
    ".reduceByKey(lambda x,y:(x+y))\\\n",
    ".sortBy(lambda x : (x[1],x),ascending=False)\\\n",
    ".collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd = pandas.DataFrame(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总计有车辆数： 8028\n"
     ]
    }
   ],
   "source": [
    "print(\"总计有车辆数：\",len(pd))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 排名前20的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>89cac57f1d6028aeb9d702fa353bff86</td>\n",
       "      <td>3963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e8fe4791e9a043dcf3fb567d5969a327</td>\n",
       "      <td>3790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6877c4b90ec348a5cbb226ce0790aa5c</td>\n",
       "      <td>1095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e03b8ea935007d83886601be21603633</td>\n",
       "      <td>794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35e6a4c4f5b4f9379d16a7133d24cae1</td>\n",
       "      <td>726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6d3724ea10543db709eaab5c820170b0</td>\n",
       "      <td>721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>75fe982f37b768e348eadcec92d080c4</td>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7143510ffdbd5bef813a244e1981c5e4</td>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4a3c796403a2bb1b921ba79b4331e7f2</td>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>bdd7ab02a9bbc5ffb9b2735185798ecc</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4b1e00972d1e8946218672fc874b0265</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>931b14172505b994b6b3d25233cd9748</td>\n",
       "      <td>699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>294acbe75403ed1207a9a02c889269f7</td>\n",
       "      <td>699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>f11a35e8f494717c6a77d9741c8ad0b3</td>\n",
       "      <td>694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>47c4ebd82898a4d3f9754b3d44f7281c</td>\n",
       "      <td>576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>c1d77c74af8dafe6ca7a68670d564aed</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0a43970b7752c37ba50a26bd484751c7</td>\n",
       "      <td>466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>d7976715780f0f05d5fcf4cd047445e9</td>\n",
       "      <td>462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>d513fd32e5bef00b4b90a3d1bd3734be</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ff3b2e9bdcc4880a72798a8d1c7fad11</td>\n",
       "      <td>409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0     1\n",
       "0   89cac57f1d6028aeb9d702fa353bff86  3963\n",
       "1   e8fe4791e9a043dcf3fb567d5969a327  3790\n",
       "2   6877c4b90ec348a5cbb226ce0790aa5c  1095\n",
       "3   e03b8ea935007d83886601be21603633   794\n",
       "4   35e6a4c4f5b4f9379d16a7133d24cae1   726\n",
       "5   6d3724ea10543db709eaab5c820170b0   721\n",
       "6   75fe982f37b768e348eadcec92d080c4   702\n",
       "7   7143510ffdbd5bef813a244e1981c5e4   701\n",
       "8   4a3c796403a2bb1b921ba79b4331e7f2   701\n",
       "9   bdd7ab02a9bbc5ffb9b2735185798ecc   700\n",
       "10  4b1e00972d1e8946218672fc874b0265   700\n",
       "11  931b14172505b994b6b3d25233cd9748   699\n",
       "12  294acbe75403ed1207a9a02c889269f7   699\n",
       "13  f11a35e8f494717c6a77d9741c8ad0b3   694\n",
       "14  47c4ebd82898a4d3f9754b3d44f7281c   576\n",
       "15  c1d77c74af8dafe6ca7a68670d564aed   467\n",
       "16  0a43970b7752c37ba50a26bd484751c7   466\n",
       "17  d7976715780f0f05d5fcf4cd047445e9   462\n",
       "18  d513fd32e5bef00b4b90a3d1bd3734be   449\n",
       "19  ff3b2e9bdcc4880a72798a8d1c7fad11   409"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mymap(line):\n",
    "    xy = line.split(\",\")\n",
    "    x = float(xy[2])\n",
    "    y = float(xy[3])\n",
    "    return (\"extent\",[x,x,y,y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = rdd.map(lambda line:mymap(line))\\\n",
    ".reduceByKey(lambda x,y : (max(x[0],y[0]),\n",
    "                           min(x[1],y[1]),\n",
    "                           max(x[2],y[2]),\n",
    "                           min(x[3],y[3]))).collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 空间范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('extent', (117.499035, 115.20195, 41.09803, 39.400021))]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 时间范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2016-01-05 00:00:02,212129de5f2e3cdb98c45361c5aebcb7,116.426608,39.949036']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdd.sortBy(lambda line : (datetime.datetime.strptime(line.split(\",\")[0], \n",
    "                                                      '%Y-%m-%d %H:%M:%S'),line),\n",
    "           ascending=True).take(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2016-01-05 10:11:32,384cf23faf81ec9d3a84006338546124,116.502436,39.988393']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdd.sortBy(lambda line : (datetime.datetime.strptime(line.split(\",\")[0], \n",
    "                                                      '%Y-%m-%d %H:%M:%S'),line),\n",
    "           ascending=False).take(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据分布密度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pygeohash\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def geohashMap(line):\n",
    "    xy = line.split(\",\")\n",
    "    return (pygeohash.encode(float(xy[3]),\n",
    "                             float(xy[2]),5),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = rdd.map(lambda line : geohashMap(line))\\\n",
    ".reduceByKey(lambda x,y : x+y)\\\n",
    ".collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = []\n",
    "y = []\n",
    "z = []\n",
    "for r in res:\n",
    "    xy = pygeohash.decode(r[0])\n",
    "    x.append(float(xy[1]))\n",
    "    y.append(float(xy[0]))\n",
    "    z.append(r[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 利用ArcGIS进行核密度分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arcpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Result 'in_memory/tempPnt'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if arcpy.Exists(\"in_memory/tempPnt\"):\n",
    "    arcpy.Delete_management(\"in_memory/tempPnt\")\n",
    "feature_class = arcpy.CreateFeatureclass_management(\"in_memory\",\n",
    "                                                    \"tempPnt\", \"POINT\")[0]\n",
    "arcpy.AddField_management(\"in_memory/tempPnt\",\"v\",\"SHORT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "with arcpy.da.InsertCursor(feature_class, [\"SHAPE@XY\",\"v\"]) as cursor:\n",
    "    for r in res:\n",
    "        xy = pygeohash.decode(r[0])\n",
    "        cursor.insertRow([(float(xy[1]), float(xy[0])),r[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "arcpy.CheckOutExtension(\"Spatial\")\n",
    "populationField = \"v\"\n",
    "cellSize = 0.001\n",
    "searchRadius = 0.1\n",
    "unit = \"SQUARE_KILOMETERS\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "outKernelDensity = arcpy.sa.KernelDensity(feature_class, populationField, cellSize,\n",
    "                                 searchRadius, unit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with arcpy.da.SearchCursor(\"./data/bj.shp\", [\"SHAPE@JSON\"]) as cursor:\n",
    "    for r in cursor:\n",
    "        bj = json.loads(r[0])[\"rings\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "desc = arcpy.Describe(outKernelDensity).extent\n",
    "fig = plt.figure(figsize=(14,9))\n",
    "plt.imshow(arcpy.RasterToNumPyArray(outKernelDensity),\n",
    "           extent=[desc.XMin,desc.XMax,desc.YMin,desc.YMax],\n",
    "           cmap =plt.cm.jet)\n",
    "ax = fig.gca()\n",
    "ax.add_patch(plt.Polygon(bj,ec=\"gray\",fc=\"None\"))\n",
    "ax.set_xlim(115.1,117.6)\n",
    "ax.set_ylim(39.3,41.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
